Overview

Dataset statistics

Number of variables85
Number of observations3472
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)0.2%
Total size in memory2.3 MiB
Average record size in memory680.0 B

Variable types

Numeric6
Categorical79

Warnings

secure_doors/windows(drzwi/okna_antywłamaniowe) has constant value "0.0" Constant
intercom/videophone(domofon/wideofon) has constant value "0.0" Constant
monitoring/security(monitoring/ochrona) has constant value "0.0" Constant
closed_area(teren_zamknięty) has constant value "0.0" Constant
garage/parking_space(garaż/miejsce_parkingowe) has constant value "0.0" Constant
only_for_non-smokers(tylko_dla_niepalących) has constant value "0.0" Constant
Dataset has 7 (0.2%) duplicate rowsDuplicates
area is highly correlated with room_num and 1 other fieldsHigh correlation
room_num is highly correlated with area and 1 other fieldsHigh correlation
floor is highly correlated with total_floorHigh correlation
total_floor is highly correlated with floorHigh correlation
fridge(lodówka) is highly correlated with furniture(meble) and 2 other fieldsHigh correlation
furniture(meble) is highly correlated with fridge(lodówka) and 1 other fieldsHigh correlation
stove(kuchenka) is highly correlated with fridge(lodówka) and 1 other fieldsHigh correlation
washer(pralka) is highly correlated with fridge(lodówka) and 2 other fieldsHigh correlation
internet is highly correlated with cable TV(telewizja kablowa)High correlation
cable TV(telewizja kablowa) is highly correlated with internetHigh correlation
build_type_Apartment_high_q(apartamentowiec) is highly correlated with build_type_Apartment_medium_q(blok)High correlation
build_type_Apartment_medium_q(blok) is highly correlated with build_type_Apartment_high_q(apartamentowiec)High correlation
windows_Plastic(plastikowe) is highly correlated with windows_Wooden(drewniane)High correlation
windows_Wooden(drewniane) is highly correlated with windows_Plastic(plastikowe)High correlation
gross_price is highly correlated with area and 1 other fieldsHigh correlation
area is highly correlated with room_num and 1 other fieldsHigh correlation
room_num is highly correlated with area and 1 other fieldsHigh correlation
floor is highly correlated with total_floorHigh correlation
total_floor is highly correlated with floorHigh correlation
fridge(lodówka) is highly correlated with furniture(meble) and 2 other fieldsHigh correlation
furniture(meble) is highly correlated with fridge(lodówka) and 1 other fieldsHigh correlation
stove(kuchenka) is highly correlated with fridge(lodówka) and 1 other fieldsHigh correlation
washer(pralka) is highly correlated with fridge(lodówka) and 2 other fieldsHigh correlation
internet is highly correlated with cable TV(telewizja kablowa)High correlation
cable TV(telewizja kablowa) is highly correlated with internetHigh correlation
build_type_Apartment_high_q(apartamentowiec) is highly correlated with build_type_Apartment_medium_q(blok)High correlation
build_type_Apartment_medium_q(blok) is highly correlated with build_type_Apartment_high_q(apartamentowiec)High correlation
windows_Plastic(plastikowe) is highly correlated with windows_Wooden(drewniane)High correlation
windows_Wooden(drewniane) is highly correlated with windows_Plastic(plastikowe)High correlation
gross_price is highly correlated with area and 1 other fieldsHigh correlation
area is highly correlated with room_num and 1 other fieldsHigh correlation
room_num is highly correlated with area and 1 other fieldsHigh correlation
fridge(lodówka) is highly correlated with furniture(meble) and 2 other fieldsHigh correlation
furniture(meble) is highly correlated with fridge(lodówka) and 1 other fieldsHigh correlation
stove(kuchenka) is highly correlated with fridge(lodówka) and 1 other fieldsHigh correlation
washer(pralka) is highly correlated with fridge(lodówka) and 2 other fieldsHigh correlation
internet is highly correlated with cable TV(telewizja kablowa)High correlation
cable TV(telewizja kablowa) is highly correlated with internetHigh correlation
build_type_Apartment_high_q(apartamentowiec) is highly correlated with build_type_Apartment_medium_q(blok)High correlation
build_type_Apartment_medium_q(blok) is highly correlated with build_type_Apartment_high_q(apartamentowiec)High correlation
windows_Plastic(plastikowe) is highly correlated with windows_Wooden(drewniane)High correlation
windows_Wooden(drewniane) is highly correlated with windows_Plastic(plastikowe)High correlation
gross_price is highly correlated with area and 1 other fieldsHigh correlation
internet is highly correlated with telephone(telefon) and 1 other fieldsHigh correlation
gross_price is highly correlated with area and 1 other fieldsHigh correlation
washer(pralka) is highly correlated with stove(kuchenka) and 2 other fieldsHigh correlation
telephone(telefon) is highly correlated with internet and 1 other fieldsHigh correlation
build_type_Apartment_high_q(apartamentowiec) is highly correlated with build_type_Apartment_medium_q(blok)High correlation
stove(kuchenka) is highly correlated with washer(pralka) and 2 other fieldsHigh correlation
cable TV(telewizja kablowa) is highly correlated with internet and 1 other fieldsHigh correlation
heating_Gas(gazowe) is highly correlated with heating_Central(miejskie)High correlation
windows_Wooden(drewniane) is highly correlated with windows_Plastic(plastikowe)High correlation
floor is highly correlated with total_floorHigh correlation
furniture(meble) is highly correlated with washer(pralka) and 2 other fieldsHigh correlation
build_type_Apartment_medium_q(blok) is highly correlated with build_type_Apartment_high_q(apartamentowiec) and 1 other fieldsHigh correlation
area is highly correlated with gross_price and 1 other fieldsHigh correlation
elevator(winda) is highly correlated with total_floorHigh correlation
build_type_Tenement(kamienica) is highly correlated with build_type_Apartment_medium_q(blok)High correlation
windows_Plastic(plastikowe) is highly correlated with windows_Wooden(drewniane)High correlation
total_floor is highly correlated with floor and 1 other fieldsHigh correlation
heating_Central(miejskie) is highly correlated with heating_Gas(gazowe)High correlation
room_num is highly correlated with gross_price and 1 other fieldsHigh correlation
fridge(lodówka) is highly correlated with washer(pralka) and 3 other fieldsHigh correlation
oven(piekarnik) is highly correlated with fridge(lodówka)High correlation
internet is highly correlated with closed_area(teren_zamknięty) and 6 other fieldsHigh correlation
build_mat_Other(inne) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ mazowieckie is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Żoliborz is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_type_Infill(plomba) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Metro Wilanowska is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Praga-Południe is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_type_Private_house_1_fam(dom wolnostojący) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
washer(pralka) is highly correlated with closed_area(teren_zamknięty) and 8 other fieldsHigh correlation
district_ Ursus is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Białołęka is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_type_Apartment_high_q(apartamentowiec) is highly correlated with closed_area(teren_zamknięty) and 6 other fieldsHigh correlation
build_type_Loft/attic(loft) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Conreate_slab(wielka_płyta) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
terrace(taras) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
closed_area(teren_zamknięty) is highly correlated with internet and 77 other fieldsHigh correlation
build_mat_Concreate(beton) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
intercom/videophone(domofon/wideofon) is highly correlated with internet and 77 other fieldsHigh correlation
district_ Wawer is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
heating_Gas(gazowe) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
heating_Boiler(kotłownia) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Praga-Północ is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
status_Renovation(do remontu) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
garden(ogródek) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
anti-burglary blinds(rolety antywłamaniowe) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Śródmieście is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
garage/parking_space(garaż/miejsce_parkingowe) is highly correlated with internet and 77 other fieldsHigh correlation
district_ Wilanów is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Bemowo is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Autoclaved_aerated_concrete(beton_komórkowy) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
furniture(meble) is highly correlated with washer(pralka) and 7 other fieldsHigh correlation
elevator(winda) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
windows_Aluminum(aluminiowe) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Reinforced_concrete(żelbet) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Ursynów is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
windows_Plastic(plastikowe) is highly correlated with closed_area(teren_zamknięty) and 6 other fieldsHigh correlation
district_ Wola is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
heating_Central(miejskie) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
heating_Electric(elektryczne) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
balcony(balkon) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
two-level(dwupoziomowe) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Mokotów is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
basement(piwnica) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Brick(cegła) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Rembertów is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Targówek is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Centrum is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
status_Not_ready_yet(do_wykończenia) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_type_Private_house_1+_fam(szeregowiec) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
monitoring/security(monitoring/ochrona) is highly correlated with internet and 77 other fieldsHigh correlation
district_ Ochota is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Wood(drewno) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
available for students(wynajmę również studentom) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Silicate brick(silikat) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Wesoła is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Expanded_clay(keramzyt) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
telephone(telefon) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Bielany is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_mat_Concrete_masonry_unit(pustak) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
stove(kuchenka) is highly correlated with washer(pralka) and 7 other fieldsHigh correlation
poddasze is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
alarm system(system alarmowy) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
cable TV(telewizja kablowa) is highly correlated with internet and 6 other fieldsHigh correlation
dish_washer(zmywarka) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
status_Ready(do_zamieszkania) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
separate kitchen(oddzielna kuchnia) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
district_ Warszawa is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
windows_Wooden(drewniane) is highly correlated with closed_area(teren_zamknięty) and 6 other fieldsHigh correlation
secure_doors/windows(drzwi/okna_antywłamaniowe) is highly correlated with internet and 77 other fieldsHigh correlation
build_type_Apartment_medium_q(blok) is highly correlated with build_type_Apartment_high_q(apartamentowiec) and 6 other fieldsHigh correlation
tv_set(telewizor) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
build_type_Tenement(kamienica) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
utility room(pom. użytkowe) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
air conditioning(klimatyzacja) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
heating_Other(inne) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
only_for_non-smokers(tylko_dla_niepalących) is highly correlated with internet and 77 other fieldsHigh correlation
district_ Włochy is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
fridge(lodówka) is highly correlated with washer(pralka) and 8 other fieldsHigh correlation
oven(piekarnik) is highly correlated with closed_area(teren_zamknięty) and 5 other fieldsHigh correlation
year_built is highly skewed (γ1 = 45.58472383) Skewed

Reproduction

Analysis started2021-05-25 11:06:42.233093
Analysis finished2021-05-25 11:07:28.796975
Duration46.56 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct117
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.45766129
Minimum3
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 KiB
2021-05-25T13:07:28.879698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile25
Q135
median42
Q353
95-th percentile76
Maximum220
Range217
Interquartile range (IQR)18

Descriptive statistics

Standard deviation17.59378521
Coefficient of variation (CV)0.3870367439
Kurtosis10.61048437
Mean45.45766129
Median Absolute Deviation (MAD)8
Skewness2.150144635
Sum157829
Variance309.5412781
MonotonicityNot monotonic
2021-05-25T13:07:28.998312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50175
 
5.0%
40174
 
5.0%
38167
 
4.8%
42127
 
3.7%
30120
 
3.5%
45111
 
3.2%
35108
 
3.1%
37104
 
3.0%
48102
 
2.9%
3695
 
2.7%
Other values (107)2189
63.0%
ValueCountFrequency (%)
31
 
< 0.1%
103
 
0.1%
125
0.1%
131
 
< 0.1%
142
 
0.1%
152
 
0.1%
1610
0.3%
179
0.3%
1811
0.3%
199
0.3%
ValueCountFrequency (%)
2201
 
< 0.1%
2001
 
< 0.1%
1811
 
< 0.1%
1801
 
< 0.1%
1661
 
< 0.1%
1601
 
< 0.1%
1501
 
< 0.1%
1471
 
< 0.1%
1403
0.1%
1341
 
< 0.1%

room_num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.98531106
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 KiB
2021-05-25T13:07:29.150606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7464987639
Coefficient of variation (CV)0.3760109834
Kurtosis1.734106779
Mean1.98531106
Median Absolute Deviation (MAD)0
Skewness0.8053912206
Sum6893
Variance0.5572604045
MonotonicityNot monotonic
2021-05-25T13:07:29.237379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21990
57.3%
1834
24.0%
3533
 
15.4%
498
 
2.8%
515
 
0.4%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
1834
24.0%
21990
57.3%
3533
 
15.4%
498
 
2.8%
515
 
0.4%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
61
 
< 0.1%
515
 
0.4%
498
 
2.8%
3533
 
15.4%
21990
57.3%
1834
24.0%

floor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.525101141
Minimum0
Maximum15
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size27.2 KiB
2021-05-25T13:07:29.345731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q35
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.612843215
Coefficient of variation (CV)0.7412108504
Kurtosis0.6993925188
Mean3.525101141
Median Absolute Deviation (MAD)2
Skewness1.160178722
Sum12239.15116
Variance6.826949665
MonotonicityNot monotonic
2021-05-25T13:07:29.463540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1945
27.2%
2604
17.4%
3517
14.9%
4369
 
10.6%
5265
 
7.6%
6205
 
5.9%
7134
 
3.9%
8111
 
3.2%
1186
 
2.5%
980
 
2.3%
Other values (50)156
 
4.5%
ValueCountFrequency (%)
04
 
0.1%
1945
27.2%
2604
17.4%
2.8139534881
 
< 0.1%
3517
14.9%
3.0465116281
 
< 0.1%
3.0581395351
 
< 0.1%
3.0930232562
 
0.1%
3.1046511631
 
< 0.1%
3.1511627911
 
< 0.1%
ValueCountFrequency (%)
151
 
< 0.1%
1186
 
2.5%
1071
 
2.0%
980
 
2.3%
8111
3.2%
7134
3.9%
6205
5.9%
5265
7.6%
4.2906976741
 
< 0.1%
4.2093023261
 
< 0.1%

total_floor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct171
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.530389294
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 KiB
2021-05-25T13:07:29.583392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q38
95-th percentile13
Maximum30
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.336553173
Coefficient of variation (CV)0.5109271474
Kurtosis4.146994142
Mean6.530389294
Median Absolute Deviation (MAD)2
Skewness1.496243399
Sum22673.51163
Variance11.13258708
MonotonicityNot monotonic
2021-05-25T13:07:29.703326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4617
17.8%
6387
11.1%
3343
9.9%
5314
9.0%
10301
8.7%
7265
 
7.6%
8234
 
6.7%
2130
 
3.7%
1586
 
2.5%
1185
 
2.4%
Other values (161)710
20.4%
ValueCountFrequency (%)
128
 
0.8%
2130
 
3.7%
3343
9.9%
4617
17.8%
5314
9.0%
5.1511627911
 
< 0.1%
5.1627906981
 
< 0.1%
5.1744186051
 
< 0.1%
5.1976744191
 
< 0.1%
5.2209302331
 
< 0.1%
ValueCountFrequency (%)
303
 
0.1%
252
 
0.1%
241
 
< 0.1%
231
 
< 0.1%
226
 
0.2%
211
 
< 0.1%
202
 
0.1%
193
 
0.1%
181
 
< 0.1%
1719
0.5%

year_built
Real number (ℝ≥0)

SKEWED

Distinct796
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.896005
Minimum1890
Maximum20202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 KiB
2021-05-25T13:07:29.822368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1890
5-th percentile1950
Q11992
median2004.319767
Q32017
95-th percentile2020
Maximum20202
Range18312
Interquartile range (IQR)25

Descriptive statistics

Standard deviation353.9073609
Coefficient of variation (CV)0.17599486
Kurtosis2192.941651
Mean2010.896005
Median Absolute Deviation (MAD)12.43604651
Skewness45.58472383
Sum6981830.93
Variance125250.4201
MonotonicityNot monotonic
2021-05-25T13:07:29.950082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020334
 
9.6%
2019164
 
4.7%
2018129
 
3.7%
2017116
 
3.3%
200893
 
2.7%
201091
 
2.6%
201689
 
2.6%
197077
 
2.2%
201575
 
2.2%
200068
 
2.0%
Other values (786)2236
64.4%
ValueCountFrequency (%)
18902
0.1%
19002
0.1%
19012
0.1%
19031
 
< 0.1%
19042
0.1%
19051
 
< 0.1%
19061
 
< 0.1%
19102
0.1%
19124
0.1%
19132
0.1%
ValueCountFrequency (%)
202021
 
< 0.1%
119761
 
< 0.1%
2219.2441861
 
< 0.1%
2217.4186051
 
< 0.1%
2217.3720933
0.1%
2217.0697671
 
< 0.1%
2216.9069771
 
< 0.1%
2216.5697671
 
< 0.1%
2216.4767441
 
< 0.1%
2215.9883721
 
< 0.1%

poddasze
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3465 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03465
99.8%
1.07
 
0.2%

Length

2021-05-25T13:07:30.157092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:30.215467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03465
99.8%
1.07
 
0.2%

Most occurring characters

ValueCountFrequency (%)
06937
66.6%
.3472
33.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06937
99.9%
17
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06937
66.6%
.3472
33.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06937
66.6%
.3472
33.3%
17
 
0.1%

dish_washer(zmywarka)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
2160 
0.0
1312 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02160
62.2%
0.01312
37.8%

Length

2021-05-25T13:07:30.357532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:30.413865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02160
62.2%
0.01312
37.8%

Most occurring characters

ValueCountFrequency (%)
04784
45.9%
.3472
33.3%
12160
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04784
68.9%
12160
31.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04784
45.9%
.3472
33.3%
12160
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04784
45.9%
.3472
33.3%
12160
20.7%

fridge(lodówka)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
3374 
0.0
 
98

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.03374
97.2%
0.098
 
2.8%

Length

2021-05-25T13:07:30.563184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:30.619307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.03374
97.2%
0.098
 
2.8%

Most occurring characters

ValueCountFrequency (%)
03570
34.3%
.3472
33.3%
13374
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03570
51.4%
13374
48.6%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03570
34.3%
.3472
33.3%
13374
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03570
34.3%
.3472
33.3%
13374
32.4%

furniture(meble)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
3259 
0.0
 
213

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.03259
93.9%
0.0213
 
6.1%

Length

2021-05-25T13:07:30.764376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:31.008317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.03259
93.9%
0.0213
 
6.1%

Most occurring characters

ValueCountFrequency (%)
03685
35.4%
.3472
33.3%
13259
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03685
53.1%
13259
46.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03685
35.4%
.3472
33.3%
13259
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03685
35.4%
.3472
33.3%
13259
31.3%

oven(piekarnik)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
2898 
0.0
574 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02898
83.5%
0.0574
 
16.5%

Length

2021-05-25T13:07:31.169564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:31.241118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02898
83.5%
0.0574
 
16.5%

Most occurring characters

ValueCountFrequency (%)
04046
38.8%
.3472
33.3%
12898
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04046
58.3%
12898
41.7%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04046
38.8%
.3472
33.3%
12898
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04046
38.8%
.3472
33.3%
12898
27.8%

stove(kuchenka)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
3272 
0.0
 
200

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.03272
94.2%
0.0200
 
5.8%

Length

2021-05-25T13:07:31.393103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:31.452871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.03272
94.2%
0.0200
 
5.8%

Most occurring characters

ValueCountFrequency (%)
03672
35.3%
.3472
33.3%
13272
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03672
52.9%
13272
47.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03672
35.3%
.3472
33.3%
13272
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03672
35.3%
.3472
33.3%
13272
31.4%

tv_set(telewizor)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
1817 
1.0
1655 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01817
52.3%
1.01655
47.7%

Length

2021-05-25T13:07:31.616847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:31.675823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01817
52.3%
1.01655
47.7%

Most occurring characters

ValueCountFrequency (%)
05289
50.8%
.3472
33.3%
11655
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05289
76.2%
11655
 
23.8%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05289
50.8%
.3472
33.3%
11655
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05289
50.8%
.3472
33.3%
11655
 
15.9%

washer(pralka)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
3319 
0.0
 
153

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.03319
95.6%
0.0153
 
4.4%

Length

2021-05-25T13:07:31.824424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:31.881910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.03319
95.6%
0.0153
 
4.4%

Most occurring characters

ValueCountFrequency (%)
03625
34.8%
.3472
33.3%
13319
31.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03625
52.2%
13319
47.8%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03625
34.8%
.3472
33.3%
13319
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03625
34.8%
.3472
33.3%
13319
31.9%

secure_doors/windows(drzwi/okna_antywłamaniowe)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3472 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03472
100.0%

Length

2021-05-25T13:07:32.025366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:32.081430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03472
100.0%

Most occurring characters

ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06944
100.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

intercom/videophone(domofon/wideofon)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3472 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03472
100.0%

Length

2021-05-25T13:07:32.219711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:32.275003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03472
100.0%

Most occurring characters

ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06944
100.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

monitoring/security(monitoring/ochrona)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3472 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03472
100.0%

Length

2021-05-25T13:07:32.411447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:32.466543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03472
100.0%

Most occurring characters

ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06944
100.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

closed_area(teren_zamknięty)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3472 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03472
100.0%

Length

2021-05-25T13:07:32.601571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:32.656238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03472
100.0%

Most occurring characters

ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06944
100.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

balcony(balkon)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
2358 
0.0
1114 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.02358
67.9%
0.01114
32.1%

Length

2021-05-25T13:07:32.800248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:32.859527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02358
67.9%
0.01114
32.1%

Most occurring characters

ValueCountFrequency (%)
04586
44.0%
.3472
33.3%
12358
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04586
66.0%
12358
34.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04586
44.0%
.3472
33.3%
12358
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04586
44.0%
.3472
33.3%
12358
22.6%

basement(piwnica)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2683 
1.0
789 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.02683
77.3%
1.0789
 
22.7%

Length

2021-05-25T13:07:33.022818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:33.106468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02683
77.3%
1.0789
 
22.7%

Most occurring characters

ValueCountFrequency (%)
06155
59.1%
.3472
33.3%
1789
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06155
88.6%
1789
 
11.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06155
59.1%
.3472
33.3%
1789
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06155
59.1%
.3472
33.3%
1789
 
7.6%

garage/parking_space(garaż/miejsce_parkingowe)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3472 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03472
100.0%

Length

2021-05-25T13:07:33.290227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:33.359066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03472
100.0%

Most occurring characters

ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06944
100.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

alarm system(system alarmowy)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3319 
1.0
 
153

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03319
95.6%
1.0153
 
4.4%

Length

2021-05-25T13:07:33.515098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:33.577496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03319
95.6%
1.0153
 
4.4%

Most occurring characters

ValueCountFrequency (%)
06791
65.2%
.3472
33.3%
1153
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06791
97.8%
1153
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06791
65.2%
.3472
33.3%
1153
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06791
65.2%
.3472
33.3%
1153
 
1.5%

only_for_non-smokers(tylko_dla_niepalących)
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3472 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03472
100.0%

Length

2021-05-25T13:07:33.725714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:33.782818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03472
100.0%

Most occurring characters

ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06944
100.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06944
66.7%
.3472
33.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3387 
1.0
 
85

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03387
97.6%
1.085
 
2.4%

Length

2021-05-25T13:07:33.941347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:34.032914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03387
97.6%
1.085
 
2.4%

Most occurring characters

ValueCountFrequency (%)
06859
65.9%
.3472
33.3%
185
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06859
98.8%
185
 
1.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06859
65.9%
.3472
33.3%
185
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06859
65.9%
.3472
33.3%
185
 
0.8%

elevator(winda)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
2304 
0.0
1168 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02304
66.4%
0.01168
33.6%

Length

2021-05-25T13:07:34.218084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:34.284636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02304
66.4%
0.01168
33.6%

Most occurring characters

ValueCountFrequency (%)
04640
44.5%
.3472
33.3%
12304
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04640
66.8%
12304
33.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04640
44.5%
.3472
33.3%
12304
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04640
44.5%
.3472
33.3%
12304
22.1%

separate kitchen(oddzielna kuchnia)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2418 
1.0
1054 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.02418
69.6%
1.01054
30.4%

Length

2021-05-25T13:07:34.449208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:34.510831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02418
69.6%
1.01054
30.4%

Most occurring characters

ValueCountFrequency (%)
05890
56.5%
.3472
33.3%
11054
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05890
84.8%
11054
 
15.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05890
56.5%
.3472
33.3%
11054
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05890
56.5%
.3472
33.3%
11054
 
10.1%

internet
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
1902 
1.0
1570 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01902
54.8%
1.01570
45.2%

Length

2021-05-25T13:07:34.891520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:34.947633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01902
54.8%
1.01570
45.2%

Most occurring characters

ValueCountFrequency (%)
05374
51.6%
.3472
33.3%
11570
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05374
77.4%
11570
 
22.6%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05374
51.6%
.3472
33.3%
11570
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05374
51.6%
.3472
33.3%
11570
 
15.1%

cable TV(telewizja kablowa)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2118 
1.0
1354 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02118
61.0%
1.01354
39.0%

Length

2021-05-25T13:07:35.100218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:35.156390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02118
61.0%
1.01354
39.0%

Most occurring characters

ValueCountFrequency (%)
05590
53.7%
.3472
33.3%
11354
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05590
80.5%
11354
 
19.5%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05590
53.7%
.3472
33.3%
11354
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05590
53.7%
.3472
33.3%
11354
 
13.0%

telephone(telefon)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3080 
1.0
392 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03080
88.7%
1.0392
 
11.3%

Length

2021-05-25T13:07:35.300444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:35.361512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03080
88.7%
1.0392
 
11.3%

Most occurring characters

ValueCountFrequency (%)
06552
62.9%
.3472
33.3%
1392
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06552
94.4%
1392
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06552
62.9%
.3472
33.3%
1392
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06552
62.9%
.3472
33.3%
1392
 
3.8%

air conditioning(klimatyzacja)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3117 
1.0
355 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03117
89.8%
1.0355
 
10.2%

Length

2021-05-25T13:07:35.516900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:35.575103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03117
89.8%
1.0355
 
10.2%

Most occurring characters

ValueCountFrequency (%)
06589
63.3%
.3472
33.3%
1355
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06589
94.9%
1355
 
5.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06589
63.3%
.3472
33.3%
1355
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06589
63.3%
.3472
33.3%
1355
 
3.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2939 
1.0
533 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02939
84.6%
1.0533
 
15.4%

Length

2021-05-25T13:07:35.720957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:35.777868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02939
84.6%
1.0533
 
15.4%

Most occurring characters

ValueCountFrequency (%)
06411
61.5%
.3472
33.3%
1533
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06411
92.3%
1533
 
7.7%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06411
61.5%
.3472
33.3%
1533
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06411
61.5%
.3472
33.3%
1533
 
5.1%

utility room(pom. użytkowe)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3097 
1.0
375 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03097
89.2%
1.0375
 
10.8%

Length

2021-05-25T13:07:35.931059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:35.988532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03097
89.2%
1.0375
 
10.8%

Most occurring characters

ValueCountFrequency (%)
06569
63.1%
.3472
33.3%
1375
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06569
94.6%
1375
 
5.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06569
63.1%
.3472
33.3%
1375
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06569
63.1%
.3472
33.3%
1375
 
3.6%

terrace(taras)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3105 
1.0
367 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03105
89.4%
1.0367
 
10.6%

Length

2021-05-25T13:07:36.131763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:36.189824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03105
89.4%
1.0367
 
10.6%

Most occurring characters

ValueCountFrequency (%)
06577
63.1%
.3472
33.3%
1367
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06577
94.7%
1367
 
5.3%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06577
63.1%
.3472
33.3%
1367
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06577
63.1%
.3472
33.3%
1367
 
3.5%

two-level(dwupoziomowe)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3438 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03438
99.0%
1.034
 
1.0%

Length

2021-05-25T13:07:36.341065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:36.397681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03438
99.0%
1.034
 
1.0%

Most occurring characters

ValueCountFrequency (%)
06910
66.3%
.3472
33.3%
134
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06910
99.5%
134
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06910
66.3%
.3472
33.3%
134
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06910
66.3%
.3472
33.3%
134
 
0.3%

garden(ogródek)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3278 
1.0
 
194

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03278
94.4%
1.0194
 
5.6%

Length

2021-05-25T13:07:36.542479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:36.598671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03278
94.4%
1.0194
 
5.6%

Most occurring characters

ValueCountFrequency (%)
06750
64.8%
.3472
33.3%
1194
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06750
97.2%
1194
 
2.8%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06750
64.8%
.3472
33.3%
1194
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06750
64.8%
.3472
33.3%
1194
 
1.9%

build_type_Apartment_high_q(apartamentowiec)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2436 
1.0
1036 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02436
70.2%
1.01036
29.8%

Length

2021-05-25T13:07:36.751520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:36.807278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02436
70.2%
1.01036
29.8%

Most occurring characters

ValueCountFrequency (%)
05908
56.7%
.3472
33.3%
11036
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05908
85.1%
11036
 
14.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05908
56.7%
.3472
33.3%
11036
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05908
56.7%
.3472
33.3%
11036
 
9.9%

build_type_Apartment_medium_q(blok)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
1837 
0.0
1635 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.01837
52.9%
0.01635
47.1%

Length

2021-05-25T13:07:36.964232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:37.023421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01837
52.9%
0.01635
47.1%

Most occurring characters

ValueCountFrequency (%)
05107
49.0%
.3472
33.3%
11837
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05107
73.5%
11837
 
26.5%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05107
49.0%
.3472
33.3%
11837
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05107
49.0%
.3472
33.3%
11837
 
17.6%

build_type_Infill(plomba)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3469 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03469
99.9%
1.03
 
0.1%

Length

2021-05-25T13:07:37.182128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:37.238297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03469
99.9%
1.03
 
0.1%

Most occurring characters

ValueCountFrequency (%)
06941
66.6%
.3472
33.3%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06941
> 99.9%
13
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06941
66.6%
.3472
33.3%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06941
66.6%
.3472
33.3%
13
 
< 0.1%

build_type_Loft/attic(loft)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3468 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03468
99.9%
1.04
 
0.1%

Length

2021-05-25T13:07:37.388038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:37.445989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03468
99.9%
1.04
 
0.1%

Most occurring characters

ValueCountFrequency (%)
06940
66.6%
.3472
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06940
99.9%
14
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06940
66.6%
.3472
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06940
66.6%
.3472
33.3%
14
 
< 0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3462 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03462
99.7%
1.010
 
0.3%

Length

2021-05-25T13:07:37.598120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:37.656026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03462
99.7%
1.010
 
0.3%

Most occurring characters

ValueCountFrequency (%)
06934
66.6%
.3472
33.3%
110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06934
99.9%
110
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06934
66.6%
.3472
33.3%
110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06934
66.6%
.3472
33.3%
110
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3450 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03450
99.4%
1.022
 
0.6%

Length

2021-05-25T13:07:37.808234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:37.867694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03450
99.4%
1.022
 
0.6%

Most occurring characters

ValueCountFrequency (%)
06922
66.5%
.3472
33.3%
122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06922
99.7%
122
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06922
66.5%
.3472
33.3%
122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06922
66.5%
.3472
33.3%
122
 
0.2%

build_type_Tenement(kamienica)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3054 
1.0
418 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.03054
88.0%
1.0418
 
12.0%

Length

2021-05-25T13:07:38.018122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:38.078929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03054
88.0%
1.0418
 
12.0%

Most occurring characters

ValueCountFrequency (%)
06526
62.7%
.3472
33.3%
1418
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06526
94.0%
1418
 
6.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06526
62.7%
.3472
33.3%
1418
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06526
62.7%
.3472
33.3%
1418
 
4.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3416 
1.0
 
56

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03416
98.4%
1.056
 
1.6%

Length

2021-05-25T13:07:38.234831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:38.290754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03416
98.4%
1.056
 
1.6%

Most occurring characters

ValueCountFrequency (%)
06888
66.1%
.3472
33.3%
156
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06888
99.2%
156
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06888
66.1%
.3472
33.3%
156
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06888
66.1%
.3472
33.3%
156
 
0.5%

build_mat_Brick(cegła)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2022 
1.0
1450 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.02022
58.2%
1.01450
41.8%

Length

2021-05-25T13:07:38.440624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:38.498308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02022
58.2%
1.01450
41.8%

Most occurring characters

ValueCountFrequency (%)
05494
52.7%
.3472
33.3%
11450
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05494
79.1%
11450
 
20.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05494
52.7%
.3472
33.3%
11450
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05494
52.7%
.3472
33.3%
11450
 
13.9%

build_mat_Concreate(beton)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3378 
1.0
 
94

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03378
97.3%
1.094
 
2.7%

Length

2021-05-25T13:07:38.648086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:38.706056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03378
97.3%
1.094
 
2.7%

Most occurring characters

ValueCountFrequency (%)
06850
65.8%
.3472
33.3%
194
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06850
98.6%
194
 
1.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06850
65.8%
.3472
33.3%
194
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06850
65.8%
.3472
33.3%
194
 
0.9%

build_mat_Concrete_masonry_unit(pustak)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3373 
1.0
 
99

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03373
97.1%
1.099
 
2.9%

Length

2021-05-25T13:07:38.862463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:39.180012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03373
97.1%
1.099
 
2.9%

Most occurring characters

ValueCountFrequency (%)
06845
65.7%
.3472
33.3%
199
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06845
98.6%
199
 
1.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06845
65.7%
.3472
33.3%
199
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06845
65.7%
.3472
33.3%
199
 
1.0%

build_mat_Conreate_slab(wielka_płyta)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3231 
1.0
 
241

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03231
93.1%
1.0241
 
6.9%

Length

2021-05-25T13:07:39.337520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:39.401173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03231
93.1%
1.0241
 
6.9%

Most occurring characters

ValueCountFrequency (%)
06703
64.4%
.3472
33.3%
1241
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06703
96.5%
1241
 
3.5%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06703
64.4%
.3472
33.3%
1241
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06703
64.4%
.3472
33.3%
1241
 
2.3%

build_mat_Expanded_clay(keramzyt)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3470 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Length

2021-05-25T13:07:39.562172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:39.619597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06942
> 99.9%
12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

build_mat_Other(inne)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3345 
1.0
 
127

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03345
96.3%
1.0127
 
3.7%

Length

2021-05-25T13:07:39.778748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:39.836632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03345
96.3%
1.0127
 
3.7%

Most occurring characters

ValueCountFrequency (%)
06817
65.4%
.3472
33.3%
1127
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06817
98.2%
1127
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06817
65.4%
.3472
33.3%
1127
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06817
65.4%
.3472
33.3%
1127
 
1.2%

build_mat_Reinforced_concrete(żelbet)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3349 
1.0
 
123

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03349
96.5%
1.0123
 
3.5%

Length

2021-05-25T13:07:39.989659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:40.048923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03349
96.5%
1.0123
 
3.5%

Most occurring characters

ValueCountFrequency (%)
06821
65.5%
.3472
33.3%
1123
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06821
98.2%
1123
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06821
65.5%
.3472
33.3%
1123
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06821
65.5%
.3472
33.3%
1123
 
1.2%

build_mat_Silicate brick(silikat)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3433 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03433
98.9%
1.039
 
1.1%

Length

2021-05-25T13:07:40.204651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:40.263900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03433
98.9%
1.039
 
1.1%

Most occurring characters

ValueCountFrequency (%)
06905
66.3%
.3472
33.3%
139
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06905
99.4%
139
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06905
66.3%
.3472
33.3%
139
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06905
66.3%
.3472
33.3%
139
 
0.4%

build_mat_Wood(drewno)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3470 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Length

2021-05-25T13:07:40.422471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:40.482713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06942
> 99.9%
12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

windows_Aluminum(aluminiowe)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3436 
1.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03436
99.0%
1.036
 
1.0%

Length

2021-05-25T13:07:40.655695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:40.726578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03436
99.0%
1.036
 
1.0%

Most occurring characters

ValueCountFrequency (%)
06908
66.3%
.3472
33.3%
136
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06908
99.5%
136
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06908
66.3%
.3472
33.3%
136
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06908
66.3%
.3472
33.3%
136
 
0.3%

windows_Plastic(plastikowe)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
2148 
0.0
1324 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02148
61.9%
0.01324
38.1%

Length

2021-05-25T13:07:40.881133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:40.958723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02148
61.9%
0.01324
38.1%

Most occurring characters

ValueCountFrequency (%)
04796
46.0%
.3472
33.3%
12148
20.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04796
69.1%
12148
30.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04796
46.0%
.3472
33.3%
12148
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04796
46.0%
.3472
33.3%
12148
20.6%

windows_Wooden(drewniane)
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2764 
1.0
708 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02764
79.6%
1.0708
 
20.4%

Length

2021-05-25T13:07:41.125163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:41.187775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02764
79.6%
1.0708
 
20.4%

Most occurring characters

ValueCountFrequency (%)
06236
59.9%
.3472
33.3%
1708
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06236
89.8%
1708
 
10.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06236
59.9%
.3472
33.3%
1708
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06236
59.9%
.3472
33.3%
1708
 
6.8%

heating_Boiler(kotłownia)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3404 
1.0
 
68

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03404
98.0%
1.068
 
2.0%

Length

2021-05-25T13:07:41.362446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:41.429039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03404
98.0%
1.068
 
2.0%

Most occurring characters

ValueCountFrequency (%)
06876
66.0%
.3472
33.3%
168
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06876
99.0%
168
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06876
66.0%
.3472
33.3%
168
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06876
66.0%
.3472
33.3%
168
 
0.7%

heating_Central(miejskie)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
2775 
0.0
697 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02775
79.9%
0.0697
 
20.1%

Length

2021-05-25T13:07:41.592514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:41.660929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.02775
79.9%
0.0697
 
20.1%

Most occurring characters

ValueCountFrequency (%)
04169
40.0%
.3472
33.3%
12775
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04169
60.0%
12775
40.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
04169
40.0%
.3472
33.3%
12775
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04169
40.0%
.3472
33.3%
12775
26.6%

heating_Electric(elektryczne)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3450 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03450
99.4%
1.022
 
0.6%

Length

2021-05-25T13:07:41.839035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:41.904309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03450
99.4%
1.022
 
0.6%

Most occurring characters

ValueCountFrequency (%)
06922
66.5%
.3472
33.3%
122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06922
99.7%
122
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06922
66.5%
.3472
33.3%
122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06922
66.5%
.3472
33.3%
122
 
0.2%

heating_Gas(gazowe)
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3363 
1.0
 
109

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03363
96.9%
1.0109
 
3.1%

Length

2021-05-25T13:07:42.079029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:42.144301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03363
96.9%
1.0109
 
3.1%

Most occurring characters

ValueCountFrequency (%)
06835
65.6%
.3472
33.3%
1109
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06835
98.4%
1109
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06835
65.6%
.3472
33.3%
1109
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06835
65.6%
.3472
33.3%
1109
 
1.0%

heating_Other(inne)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3456 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03456
99.5%
1.016
 
0.5%

Length

2021-05-25T13:07:42.313368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:42.377384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03456
99.5%
1.016
 
0.5%

Most occurring characters

ValueCountFrequency (%)
06928
66.5%
.3472
33.3%
116
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06928
99.8%
116
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06928
66.5%
.3472
33.3%
116
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06928
66.5%
.3472
33.3%
116
 
0.2%

status_Not_ready_yet(do_wykończenia)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3466 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03466
99.8%
1.06
 
0.2%

Length

2021-05-25T13:07:42.549773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:42.615895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03466
99.8%
1.06
 
0.2%

Most occurring characters

ValueCountFrequency (%)
06938
66.6%
.3472
33.3%
16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06938
99.9%
16
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06938
66.6%
.3472
33.3%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06938
66.6%
.3472
33.3%
16
 
0.1%

status_Ready(do_zamieszkania)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
1.0
3175 
0.0
 
297

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.03175
91.4%
0.0297
 
8.6%

Length

2021-05-25T13:07:42.785271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:42.855374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.03175
91.4%
0.0297
 
8.6%

Most occurring characters

ValueCountFrequency (%)
03769
36.2%
.3472
33.3%
13175
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03769
54.3%
13175
45.7%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03769
36.2%
.3472
33.3%
13175
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03769
36.2%
.3472
33.3%
13175
30.5%

status_Renovation(do remontu)
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3470 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Length

2021-05-25T13:07:43.050982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:43.120618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06942
> 99.9%
12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

district_ Bemowo
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3317 
1.0
 
155

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03317
95.5%
1.0155
 
4.5%

Length

2021-05-25T13:07:43.296042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:43.363960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03317
95.5%
1.0155
 
4.5%

Most occurring characters

ValueCountFrequency (%)
06789
65.2%
.3472
33.3%
1155
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06789
97.8%
1155
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06789
65.2%
.3472
33.3%
1155
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06789
65.2%
.3472
33.3%
1155
 
1.5%

district_ Białołęka
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3317 
1.0
 
155

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03317
95.5%
1.0155
 
4.5%

Length

2021-05-25T13:07:43.546158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:43.615772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03317
95.5%
1.0155
 
4.5%

Most occurring characters

ValueCountFrequency (%)
06789
65.2%
.3472
33.3%
1155
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06789
97.8%
1155
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06789
65.2%
.3472
33.3%
1155
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06789
65.2%
.3472
33.3%
1155
 
1.5%

district_ Bielany
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3306 
1.0
 
166

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03306
95.2%
1.0166
 
4.8%

Length

2021-05-25T13:07:43.791553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:43.860503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03306
95.2%
1.0166
 
4.8%

Most occurring characters

ValueCountFrequency (%)
06778
65.1%
.3472
33.3%
1166
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06778
97.6%
1166
 
2.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06778
65.1%
.3472
33.3%
1166
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06778
65.1%
.3472
33.3%
1166
 
1.6%

district_ Centrum
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3412 
1.0
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03412
98.3%
1.060
 
1.7%

Length

2021-05-25T13:07:44.042919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:44.110335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03412
98.3%
1.060
 
1.7%

Most occurring characters

ValueCountFrequency (%)
06884
66.1%
.3472
33.3%
160
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06884
99.1%
160
 
0.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06884
66.1%
.3472
33.3%
160
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06884
66.1%
.3472
33.3%
160
 
0.6%

district_ Metro Wilanowska
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3470 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Length

2021-05-25T13:07:44.290369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:44.359033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03470
99.9%
1.02
 
0.1%

Most occurring characters

ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06942
> 99.9%
12
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06942
66.6%
.3472
33.3%
12
 
< 0.1%

district_ Mokotów
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2825 
1.0
647 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02825
81.4%
1.0647
 
18.6%

Length

2021-05-25T13:07:44.534718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:44.608368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02825
81.4%
1.0647
 
18.6%

Most occurring characters

ValueCountFrequency (%)
06297
60.5%
.3472
33.3%
1647
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06297
90.7%
1647
 
9.3%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06297
60.5%
.3472
33.3%
1647
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06297
60.5%
.3472
33.3%
1647
 
6.2%

district_ Ochota
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3271 
1.0
 
201

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.03271
94.2%
1.0201
 
5.8%

Length

2021-05-25T13:07:44.813415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:44.883823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03271
94.2%
1.0201
 
5.8%

Most occurring characters

ValueCountFrequency (%)
06743
64.7%
.3472
33.3%
1201
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06743
97.1%
1201
 
2.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06743
64.7%
.3472
33.3%
1201
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06743
64.7%
.3472
33.3%
1201
 
1.9%

district_ Praga-Południe
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3233 
1.0
 
239

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03233
93.1%
1.0239
 
6.9%

Length

2021-05-25T13:07:45.418313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:45.489753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03233
93.1%
1.0239
 
6.9%

Most occurring characters

ValueCountFrequency (%)
06705
64.4%
.3472
33.3%
1239
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06705
96.6%
1239
 
3.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06705
64.4%
.3472
33.3%
1239
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06705
64.4%
.3472
33.3%
1239
 
2.3%

district_ Praga-Północ
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3337 
1.0
 
135

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03337
96.1%
1.0135
 
3.9%

Length

2021-05-25T13:07:45.673092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:45.744033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03337
96.1%
1.0135
 
3.9%

Most occurring characters

ValueCountFrequency (%)
06809
65.4%
.3472
33.3%
1135
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06809
98.1%
1135
 
1.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06809
65.4%
.3472
33.3%
1135
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06809
65.4%
.3472
33.3%
1135
 
1.3%

district_ Rembertów
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3458 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03458
99.6%
1.014
 
0.4%

Length

2021-05-25T13:07:45.929735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:46.000621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03458
99.6%
1.014
 
0.4%

Most occurring characters

ValueCountFrequency (%)
06930
66.5%
.3472
33.3%
114
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06930
99.8%
114
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06930
66.5%
.3472
33.3%
114
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06930
66.5%
.3472
33.3%
114
 
0.1%

district_ Targówek
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3356 
1.0
 
116

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03356
96.7%
1.0116
 
3.3%

Length

2021-05-25T13:07:46.191245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:46.262998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03356
96.7%
1.0116
 
3.3%

Most occurring characters

ValueCountFrequency (%)
06828
65.6%
.3472
33.3%
1116
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06828
98.3%
1116
 
1.7%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06828
65.6%
.3472
33.3%
1116
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06828
65.6%
.3472
33.3%
1116
 
1.1%

district_ Ursus
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3375 
1.0
 
97

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03375
97.2%
1.097
 
2.8%

Length

2021-05-25T13:07:46.450713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:46.522825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03375
97.2%
1.097
 
2.8%

Most occurring characters

ValueCountFrequency (%)
06847
65.7%
.3472
33.3%
197
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06847
98.6%
197
 
1.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06847
65.7%
.3472
33.3%
197
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06847
65.7%
.3472
33.3%
197
 
0.9%

district_ Ursynów
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3267 
1.0
 
205

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03267
94.1%
1.0205
 
5.9%

Length

2021-05-25T13:07:46.719750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:46.796152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03267
94.1%
1.0205
 
5.9%

Most occurring characters

ValueCountFrequency (%)
06739
64.7%
.3472
33.3%
1205
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06739
97.0%
1205
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06739
64.7%
.3472
33.3%
1205
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06739
64.7%
.3472
33.3%
1205
 
2.0%

district_ Warszawa
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3453 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03453
99.5%
1.019
 
0.5%

Length

2021-05-25T13:07:47.030297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:47.107756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03453
99.5%
1.019
 
0.5%

Most occurring characters

ValueCountFrequency (%)
06925
66.5%
.3472
33.3%
119
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06925
99.7%
119
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06925
66.5%
.3472
33.3%
119
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06925
66.5%
.3472
33.3%
119
 
0.2%

district_ Wawer
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3430 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03430
98.8%
1.042
 
1.2%

Length

2021-05-25T13:07:47.314628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:47.394138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03430
98.8%
1.042
 
1.2%

Most occurring characters

ValueCountFrequency (%)
06902
66.3%
.3472
33.3%
142
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06902
99.4%
142
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06902
66.3%
.3472
33.3%
142
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06902
66.3%
.3472
33.3%
142
 
0.4%

district_ Wesoła
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3458 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03458
99.6%
1.014
 
0.4%

Length

2021-05-25T13:07:47.599591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:47.678078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03458
99.6%
1.014
 
0.4%

Most occurring characters

ValueCountFrequency (%)
06930
66.5%
.3472
33.3%
114
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06930
99.8%
114
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06930
66.5%
.3472
33.3%
114
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06930
66.5%
.3472
33.3%
114
 
0.1%

district_ Wilanów
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3347 
1.0
 
125

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03347
96.4%
1.0125
 
3.6%

Length

2021-05-25T13:07:47.883341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:47.959099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03347
96.4%
1.0125
 
3.6%

Most occurring characters

ValueCountFrequency (%)
06819
65.5%
.3472
33.3%
1125
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06819
98.2%
1125
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06819
65.5%
.3472
33.3%
1125
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06819
65.5%
.3472
33.3%
1125
 
1.2%

district_ Wola
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
2960 
1.0
512 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.02960
85.3%
1.0512
 
14.7%

Length

2021-05-25T13:07:48.201005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:48.281303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02960
85.3%
1.0512
 
14.7%

Most occurring characters

ValueCountFrequency (%)
06432
61.8%
.3472
33.3%
1512
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06432
92.6%
1512
 
7.4%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06432
61.8%
.3472
33.3%
1512
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06432
61.8%
.3472
33.3%
1512
 
4.9%

district_ Włochy
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3395 
1.0
 
77

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03395
97.8%
1.077
 
2.2%

Length

2021-05-25T13:07:48.492318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:48.583790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03395
97.8%
1.077
 
2.2%

Most occurring characters

ValueCountFrequency (%)
06867
65.9%
.3472
33.3%
177
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06867
98.9%
177
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06867
65.9%
.3472
33.3%
177
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06867
65.9%
.3472
33.3%
177
 
0.7%

district_ mazowieckie
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3428 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03428
98.7%
1.044
 
1.3%

Length

2021-05-25T13:07:48.803834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:48.886996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03428
98.7%
1.044
 
1.3%

Most occurring characters

ValueCountFrequency (%)
06900
66.2%
.3472
33.3%
144
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06900
99.4%
144
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06900
66.2%
.3472
33.3%
144
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06900
66.2%
.3472
33.3%
144
 
0.4%

district_ Śródmieście
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3130 
1.0
342 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03130
90.1%
1.0342
 
9.9%

Length

2021-05-25T13:07:49.094833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:49.173100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03130
90.1%
1.0342
 
9.9%

Most occurring characters

ValueCountFrequency (%)
06602
63.4%
.3472
33.3%
1342
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06602
95.1%
1342
 
4.9%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06602
63.4%
.3472
33.3%
1342
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06602
63.4%
.3472
33.3%
1342
 
3.3%

district_ Żoliborz
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.2 KiB
0.0
3367 
1.0
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10416
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03367
97.0%
1.0105
 
3.0%

Length

2021-05-25T13:07:49.383582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-25T13:07:49.461894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03367
97.0%
1.0105
 
3.0%

Most occurring characters

ValueCountFrequency (%)
06839
65.7%
.3472
33.3%
1105
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6944
66.7%
Other Punctuation3472
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06839
98.5%
1105
 
1.5%
Other Punctuation
ValueCountFrequency (%)
.3472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10416
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06839
65.7%
.3472
33.3%
1105
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06839
65.7%
.3472
33.3%
1105
 
1.0%

gross_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1256
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2743.665343
Minimum581.4186047
Maximum11500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 KiB
2021-05-25T13:07:49.563458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum581.4186047
5-th percentile1738.40407
Q12200
median2587.476744
Q33000
95-th percentile4391.627326
Maximum11500
Range10918.5814
Interquartile range (IQR)800

Descriptive statistics

Standard deviation938.3324972
Coefficient of variation (CV)0.3419996173
Kurtosis13.4003479
Mean2743.665343
Median Absolute Deviation (MAD)412.5232558
Skewness2.709006919
Sum9526006.07
Variance880467.8754
MonotonicityNot monotonic
2021-05-25T13:07:49.720040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250095
 
2.7%
220080
 
2.3%
260063
 
1.8%
230063
 
1.8%
200062
 
1.8%
240059
 
1.7%
210056
 
1.6%
300049
 
1.4%
290044
 
1.3%
270043
 
1.2%
Other values (1246)2858
82.3%
ValueCountFrequency (%)
581.41860471
< 0.1%
588.04651161
< 0.1%
773.6279071
< 0.1%
941.06976741
< 0.1%
10321
< 0.1%
11201
< 0.1%
1140.720931
< 0.1%
11501
< 0.1%
11981
< 0.1%
11991
< 0.1%
ValueCountFrequency (%)
115001
< 0.1%
10598.267441
< 0.1%
105001
< 0.1%
100001
< 0.1%
98001
< 0.1%
9613.51
< 0.1%
9215.4186051
< 0.1%
9207.6744191
< 0.1%
8657.6744191
< 0.1%
8201.5697672
0.1%

Interactions

2021-05-25T13:07:18.223694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:18.312675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:18.405850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:18.531155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:18.663305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:18.772187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:18.866776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:18.963440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.063476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.275679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.392735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.508118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.609967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.722585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.843049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:19.953128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.089426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.233002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.369988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.482512image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.580262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.678131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.775818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.882247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:20.984794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.093190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.212553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.317973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.423948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.562810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.700124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.820019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:21.916260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:22.023530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:22.173774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-25T13:07:22.290014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-25T13:07:50.076097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-25T13:07:52.815151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-25T13:07:55.547431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-25T13:07:58.380614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-25T13:08:00.993109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-25T13:07:22.969731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-25T13:07:27.478579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

arearoom_numfloortotal_flooryear_builtpoddaszedish_washer(zmywarka)fridge(lodówka)furniture(meble)oven(piekarnik)stove(kuchenka)tv_set(telewizor)washer(pralka)secure_doors/windows(drzwi/okna_antywłamaniowe)intercom/videophone(domofon/wideofon)monitoring/security(monitoring/ochrona)closed_area(teren_zamknięty)balcony(balkon)basement(piwnica)garage/parking_space(garaż/miejsce_parkingowe)alarm system(system alarmowy)only_for_non-smokers(tylko_dla_niepalących)anti-burglary blinds(rolety antywłamaniowe)elevator(winda)separate kitchen(oddzielna kuchnia)internetcable TV(telewizja kablowa)telephone(telefon)air conditioning(klimatyzacja)available for students(wynajmę również studentom)utility room(pom. użytkowe)terrace(taras)two-level(dwupoziomowe)garden(ogródek)build_type_Apartment_high_q(apartamentowiec)build_type_Apartment_medium_q(blok)build_type_Infill(plomba)build_type_Loft/attic(loft)build_type_Private_house_1+_fam(szeregowiec)build_type_Private_house_1_fam(dom wolnostojący)build_type_Tenement(kamienica)build_mat_Autoclaved_aerated_concrete(beton_komórkowy)build_mat_Brick(cegła)build_mat_Concreate(beton)build_mat_Concrete_masonry_unit(pustak)build_mat_Conreate_slab(wielka_płyta)build_mat_Expanded_clay(keramzyt)build_mat_Other(inne)build_mat_Reinforced_concrete(żelbet)build_mat_Silicate brick(silikat)build_mat_Wood(drewno)windows_Aluminum(aluminiowe)windows_Plastic(plastikowe)windows_Wooden(drewniane)heating_Boiler(kotłownia)heating_Central(miejskie)heating_Electric(elektryczne)heating_Gas(gazowe)heating_Other(inne)status_Not_ready_yet(do_wykończenia)status_Ready(do_zamieszkania)status_Renovation(do remontu)district_ Bemowodistrict_ Białołękadistrict_ Bielanydistrict_ Centrumdistrict_ Metro Wilanowskadistrict_ Mokotówdistrict_ Ochotadistrict_ Praga-Południedistrict_ Praga-Północdistrict_ Rembertówdistrict_ Targówekdistrict_ Ursusdistrict_ Ursynówdistrict_ Warszawadistrict_ Wawerdistrict_ Wesoładistrict_ Wilanówdistrict_ Woladistrict_ Włochydistrict_ mazowieckiedistrict_ Śródmieściedistrict_ Żoliborzgross_price
037.02.01.04.0000002001.4534880.01.01.01.01.01.01.01.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.02435.244186
138.02.01.05.8023262020.0000000.01.01.01.01.01.00.01.00.00.00.00.00.00.00.00.00.00.01.00.01.01.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02175.000000
257.03.01.015.0000001984.0000000.01.01.01.01.01.01.01.00.00.00.00.01.00.00.00.00.00.00.01.01.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03300.000000
363.02.04.05.0000002005.0000000.01.01.01.01.01.01.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.02585.046512
465.03.03.04.0000001938.0000000.01.01.01.01.01.00.01.00.00.00.00.00.01.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03424.918605
550.02.01.03.0000001957.0000000.01.01.01.01.01.00.01.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.02918.383721
631.01.05.07.0000001984.4767440.00.01.01.01.01.00.01.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01955.604651
720.01.02.04.0000002000.0000000.01.01.01.00.01.01.01.00.00.00.00.00.00.00.00.00.00.01.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01900.000000
890.03.08.08.0000002020.0000000.01.01.00.01.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04700.000000
979.03.01.03.0000002004.0000000.01.01.01.01.01.00.01.00.00.00.00.01.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03000.000000

Last rows

arearoom_numfloortotal_flooryear_builtpoddaszedish_washer(zmywarka)fridge(lodówka)furniture(meble)oven(piekarnik)stove(kuchenka)tv_set(telewizor)washer(pralka)secure_doors/windows(drzwi/okna_antywłamaniowe)intercom/videophone(domofon/wideofon)monitoring/security(monitoring/ochrona)closed_area(teren_zamknięty)balcony(balkon)basement(piwnica)garage/parking_space(garaż/miejsce_parkingowe)alarm system(system alarmowy)only_for_non-smokers(tylko_dla_niepalących)anti-burglary blinds(rolety antywłamaniowe)elevator(winda)separate kitchen(oddzielna kuchnia)internetcable TV(telewizja kablowa)telephone(telefon)air conditioning(klimatyzacja)available for students(wynajmę również studentom)utility room(pom. użytkowe)terrace(taras)two-level(dwupoziomowe)garden(ogródek)build_type_Apartment_high_q(apartamentowiec)build_type_Apartment_medium_q(blok)build_type_Infill(plomba)build_type_Loft/attic(loft)build_type_Private_house_1+_fam(szeregowiec)build_type_Private_house_1_fam(dom wolnostojący)build_type_Tenement(kamienica)build_mat_Autoclaved_aerated_concrete(beton_komórkowy)build_mat_Brick(cegła)build_mat_Concreate(beton)build_mat_Concrete_masonry_unit(pustak)build_mat_Conreate_slab(wielka_płyta)build_mat_Expanded_clay(keramzyt)build_mat_Other(inne)build_mat_Reinforced_concrete(żelbet)build_mat_Silicate brick(silikat)build_mat_Wood(drewno)windows_Aluminum(aluminiowe)windows_Plastic(plastikowe)windows_Wooden(drewniane)heating_Boiler(kotłownia)heating_Central(miejskie)heating_Electric(elektryczne)heating_Gas(gazowe)heating_Other(inne)status_Not_ready_yet(do_wykończenia)status_Ready(do_zamieszkania)status_Renovation(do remontu)district_ Bemowodistrict_ Białołękadistrict_ Bielanydistrict_ Centrumdistrict_ Metro Wilanowskadistrict_ Mokotówdistrict_ Ochotadistrict_ Praga-Południedistrict_ Praga-Północdistrict_ Rembertówdistrict_ Targówekdistrict_ Ursusdistrict_ Ursynówdistrict_ Warszawadistrict_ Wawerdistrict_ Wesoładistrict_ Wilanówdistrict_ Woladistrict_ Włochydistrict_ mazowieckiedistrict_ Śródmieściedistrict_ Żoliborzgross_price
346258.02.04.06.01962.0000000.01.01.01.01.01.01.01.00.00.00.00.00.00.00.00.00.00.01.01.01.01.01.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02950.000000
346328.02.01.015.01972.0000000.00.01.01.01.01.01.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.02200.000000
346444.02.01.03.02020.0000000.01.01.01.01.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02150.000000
346540.01.01.04.02000.0000000.00.01.01.01.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01860.000000
346639.02.011.015.01995.8488370.00.01.01.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.02357.837209
346745.01.03.07.02002.9651160.01.01.01.01.01.01.01.00.00.00.00.00.00.00.00.00.00.01.00.01.01.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02350.000000
346850.02.01.02.02006.0000000.01.01.01.01.01.01.01.00.00.00.00.01.01.00.00.00.00.00.00.01.01.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.03737.302326
346946.02.05.05.02015.0000000.01.01.01.01.01.01.01.00.00.00.00.01.00.00.00.00.00.01.00.01.01.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02842.093023
347023.01.01.04.01938.0000000.00.01.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01570.000000
347141.02.01.06.01990.0000000.00.01.01.01.01.01.01.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.02499.000000

Duplicate rows

Most frequently occurring

arearoom_numfloortotal_flooryear_builtpoddaszedish_washer(zmywarka)fridge(lodówka)furniture(meble)oven(piekarnik)stove(kuchenka)tv_set(telewizor)washer(pralka)secure_doors/windows(drzwi/okna_antywłamaniowe)intercom/videophone(domofon/wideofon)monitoring/security(monitoring/ochrona)closed_area(teren_zamknięty)balcony(balkon)basement(piwnica)garage/parking_space(garaż/miejsce_parkingowe)alarm system(system alarmowy)only_for_non-smokers(tylko_dla_niepalących)anti-burglary blinds(rolety antywłamaniowe)elevator(winda)separate kitchen(oddzielna kuchnia)internetcable TV(telewizja kablowa)telephone(telefon)air conditioning(klimatyzacja)available for students(wynajmę również studentom)utility room(pom. użytkowe)terrace(taras)two-level(dwupoziomowe)garden(ogródek)build_type_Apartment_high_q(apartamentowiec)build_type_Apartment_medium_q(blok)build_type_Infill(plomba)build_type_Loft/attic(loft)build_type_Private_house_1+_fam(szeregowiec)build_type_Private_house_1_fam(dom wolnostojący)build_type_Tenement(kamienica)build_mat_Autoclaved_aerated_concrete(beton_komórkowy)build_mat_Brick(cegła)build_mat_Concreate(beton)build_mat_Concrete_masonry_unit(pustak)build_mat_Conreate_slab(wielka_płyta)build_mat_Expanded_clay(keramzyt)build_mat_Other(inne)build_mat_Reinforced_concrete(żelbet)build_mat_Silicate brick(silikat)build_mat_Wood(drewno)windows_Aluminum(aluminiowe)windows_Plastic(plastikowe)windows_Wooden(drewniane)heating_Boiler(kotłownia)heating_Central(miejskie)heating_Electric(elektryczne)heating_Gas(gazowe)heating_Other(inne)status_Not_ready_yet(do_wykończenia)status_Ready(do_zamieszkania)status_Renovation(do remontu)district_ Bemowodistrict_ Białołękadistrict_ Bielanydistrict_ Centrumdistrict_ Metro Wilanowskadistrict_ Mokotówdistrict_ Ochotadistrict_ Praga-Południedistrict_ Praga-Północdistrict_ Rembertówdistrict_ Targówekdistrict_ Ursusdistrict_ Ursynówdistrict_ Warszawadistrict_ Wawerdistrict_ Wesoładistrict_ Wilanówdistrict_ Woladistrict_ Włochydistrict_ mazowieckiedistrict_ Śródmieściedistrict_ Żoliborzgross_price# duplicates
437.02.01.0000004.01999.0232560.01.01.01.01.01.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.02673.0465123
026.01.06.0000007.02019.0000000.01.01.01.00.01.01.01.00.00.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.02200.0000002
133.01.07.00000015.02007.0000000.00.01.01.01.01.01.01.00.00.00.00.01.00.00.00.00.00.01.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02013.1744192
235.02.03.6860478.02013.0000000.01.01.01.00.01.01.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.02500.0000002
335.02.03.6860478.02013.0000000.01.01.01.00.01.01.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.02500.0000002
540.01.06.0000008.01987.7906980.00.01.01.01.01.01.01.00.00.00.00.00.01.00.00.00.00.01.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.02200.0000002
660.03.02.0000005.02019.0000000.01.01.01.01.01.00.01.00.00.00.00.01.00.00.00.00.00.01.00.01.01.00.01.00.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.03578.1976742